Abstract
The transformation of enterprises into digital entities is becoming increasingly urgent. With the trend towards digitization, many issues arise when enterprises build Laboratory Information Management Systems (LIMS) platforms, such as insufficient resources, inconsistent goals, outdated management modes, and difficulties in equipment upgrades. To address these challenges, enterprises need to implement three aspects of cognition for system setup, business management mode, and technical architecture design. This article introduces the key points of digital transformation for air-conditioning laboratories, elaborates on the data flow analysis of air-conditioning labs, and discusses the design mindset and basic infrastructure of data-energized hierarchical planning, providing a robust reference for the digital transformation of energy efficiency laboratories in refrigeration enterprises.
Keywords: Digitization; Air-Conditioning Laboratory; Energy Efficiency Quality Inspection; Data Energized.
Introduction
Since the late 1960s, Laboratory Information Management Systems (LIMS) have undergone three stages: research, development, and commercialization, expanding from the petrochemical industry to various sectors. Currently, many industries have developed mature LIMS systems that significantly contribute to the digital transformation and upgrading of enterprises. This article explores the challenges faced by the air conditioning industry in the digital transformation of laboratories within enterprises and how to carry out digital transformation based on existing construction. The goal is to create an intelligent management system based on the entire production process quality that serves enterprise quality management, providing risk monitoring and intelligent decision-making support for the production of household air conditioning products. By leveraging information technology, the aim is to optimize business processes and enhance the quality and efficiency of enterprises.
1 Significance and Resistance to Developing LIMS in Household Air Conditioning Enterprises
1.1 Significance of Digital Transformation of Laboratories in Household Air Conditioning Enterprises
In domestic manufacturing enterprises, laboratories typically participate in the inspection of supplier materials upon arrival and the final inspection before product shipment, ensuring product quality. With the introduction of mature systems such as Boom, ERP, MES, and RFID, the digital transformation revolution within enterprises is thriving. The exploration of digital transformation in household air conditioning laboratories is vigorous, employing information technology to process laboratory quality data, which not only improves internal operational efficiency but also provides strong intelligent decision-making support for product quality. Given that intelligence is still in its infancy in China, and smart parks have not yet established unified standards after over a decade of development, the industry needs to establish standards for LIMS systems. Due to the different characteristics of various business fields, developing a distinctive LIMS tailored to the enterprise is an inevitable trend and the optimal choice for independent development. In laboratories, data is the most important asset, and generating data is the most crucial task. Only through the upgrade of new-generation technologies can valuable data be formed, which can then be utilized through other means to achieve hierarchical applications of data, empowering the enterprise and providing quality decision support across all production stages.
1.2 Resistance to Digital Transformation of Laboratories in Household Air Conditioning Enterprises
1.2.1 Personnel Resistance
During the digital transformation process, enterprises face several personnel-related resistances:
- Insufficient IT Human Resources: The implementation of laboratory digital systems tends to be lengthy and of low quality. Some enterprises focus on the IT department when building air conditioning LIMS, while most manufacturing IT personnel prioritize issues related to system and instrument connectivity, or the compatibility of systems with hardware, or even personal preferences regarding development technology or interface styles. This leads to a lack of focus on critical aspects of LIMS platform construction. Additionally, the high turnover rate of programmers in the IT industry (with an average annual turnover rate exceeding 25% internationally) results in low stability for LIMS project development.
- Low Information Technology Proficiency Among Business Personnel: Business personnel’s skills have not improved alongside the enterprise’s transformation. Issues manifest as unclear initial requirements and high communication costs in product planning; later, there is a lack of enthusiasm for application promotion and delayed feedback on optimization. This results in resistance to promoting the system after white-box and black-box testing, slowing down optimization efforts and potentially stalling projects.
- High Expectations from Management Without Top-Level Design: Management personnel set high goals for laboratory digital transformation but lack top-level design, leading to a significant gap between expectations and reality, ultimately eroding confidence in transformation. Those setting the goals may not understand the business, while those who do may not be familiar with overarching planning, causing disconnects in the initial planning stages. This is due to insufficient investment of energy by management and a lack of top-down design planning for IT projects. Therefore, developing LIMS requires full participation, with leadership taking precedence, enhancing the significance placed on developers, technical skills of business personnel, and professionalism of management personnel. A complete training mechanism should be established within the enterprise, with targeted training plans and strict acceptance processes to improve the overall level of information work.
1.2.2 Challenges in Laboratory Equipment for Digital Transformation
The collection of basic and experimental data is a core part of LIMS operations. Air conditioning energy efficiency laboratories are characterized by multiple data collection points and large volumes of data processing, generating substantial experimental data due to their service to production enterprises. Traditionally, experimental data is recorded manually, leading to low data fluidity, potential accuracy issues, and low data empowerment. To achieve one-click uploading of experimental data and support intelligent decision-making, data collection from experimental equipment must be prioritized. However, some enterprises have purchased equipment without data interfaces during the initial planning stages, preventing the data generated by these devices from directly interfacing with LIMS. The solution typically involves retrofitting equipment, but the costs associated with sensors, IoT modules, communication interfaces, and data security issues pose significant challenges. The substantial costs of upgrading air conditioning energy efficiency laboratory equipment create major obstacles for digital transformation, with outdated equipment being a primary issue for enterprises. In the rapidly developing era of 5G, data collection from energy efficiency laboratory equipment can be achieved through a 5G + IoT platform model. Utilizing technologies such as 5G, edge computing, and responsive systems, data collection, computation, and communication can be realized without modifying existing equipment, using an additional 5G gateway. Additionally, a device IoT cloud platform can define device models, adapt to multiple protocols, and provide data visualization applications, offering precise data support for equipment operation monitoring. New technologies such as RPA (Robotic Process Automation) can also be applied.
1.2.3 Complexity of Internal Systems
In terms of laboratory transformation, LIMS is associated with complex internal systems of production enterprises, covering a wide range of business areas and involving significant iterative tasks. The air conditioning energy efficiency laboratory plays a crucial role in product quality control, and the demands for digital transformation of laboratories from enterprise personnel are multifaceted. On one hand, inspection data must determine the quality of finished products before shipment; on the other hand, inspection results must relate to process quality data to identify quality failure points. The laboratory system encompasses aspects such as equipment management, remote control, personnel performance, data traceability, anomaly management, quality warnings, and intelligent decision-making. Therefore, the LIMS for air conditioning enterprises must achieve vertical management while also ensuring horizontal interconnectivity, seamlessly integrating with ERP, MES, OA, and other systems to ensure that outgoing products meet relevant enterprise and national standards. If product performance is substandard, anomalies must be promptly investigated, and quality failure points rectified. Ultimately, the core business of the laboratory is quality inspection; thus, the development of laboratory systems under the context of digital transformation in air conditioning enterprises should focus on quality inspection business to structurally design the LIMS platform. This article proposes a business management architecture for energy efficiency laboratories in household air conditioning enterprises, which has proven effective in application. By adopting a layered approach to design the primary business data flow, the system functionality is modularly presented. The following diagram illustrates the laboratory business data flow analysis, where ERP automatically identifies sampling orders, arranges testing projects based on enterprise standards, and matches corresponding laboratories. A mobile app complements the enterprise web interface and experimental platform software to facilitate quality inspection, allowing testers to conveniently manage tasks such as receiving, testing, packaging, transferring, closing anomalies, unsealing finished products, and managing accounts. LIMS integrates experimental reports and manages air conditioning business data, supporting data analysis. Implementing this solution allows enterprises to achieve integration, reorganization, and optimization of existing operations.
2 Building the Digital Transformation Architecture for Air Conditioning Energy Efficiency Laboratories
2.1 Data Flow Analysis of Information Systems in Air Conditioning Energy Efficiency Laboratories
The main business of energy efficiency laboratories is to conduct performance testing on submitted products to ensure that outgoing products meet relevant enterprise and national standards. If product performance is substandard, anomalies must be promptly investigated and quality failure points rectified. The core business of the laboratory ultimately revolves around quality inspection; thus, the development of laboratory systems under the context of digital transformation in air conditioning enterprises should focus on quality inspection business to structurally design the LIMS platform. This article proposes a business management architecture for energy efficiency laboratories in household air conditioning enterprises, which has proven effective in application. By adopting a layered approach to design the primary business data flow, the system functionality is modularly presented. The following diagram illustrates the laboratory business data flow analysis, where ERP automatically identifies sampling orders, arranges testing projects based on enterprise standards, and matches corresponding laboratories. A mobile app complements the enterprise web interface and experimental platform software to facilitate quality inspection, allowing testers to conveniently manage tasks such as receiving, testing, packaging, transferring, closing anomalies, unsealing finished products, and managing accounts. LIMS integrates experimental reports and manages air conditioning business data, supporting data analysis. Implementing this solution allows enterprises to achieve integration, reorganization, and optimization of existing operations.
2.2 Key Considerations for Developing an Air Conditioning LIMS Platform
The first priority in developing a digital laboratory is to clarify objectives. With limited funding, it is essential to focus on quality management and efficiency improvements related to enterprise operations. Laboratory managers should not view all business operations as management targets for LIMS, as developing LIMS is not about creating a one-size-fits-all system. Laboratory managers should clearly recognize which tasks can be managed by LIMS to improve work efficiency and standardize work behaviors, and which tasks may be more effectively managed through other means. Therefore, during the digital transformation process of the laboratory, it is crucial to establish clear objectives and plan business operations from the top down. The LIMS platform serving production enterprises must fully leverage its broad role in quality management. First, laboratory reports are centralized feedback on testing results and should be linked to critical material production and assembly information at the front end, while also providing quality assurance for finished product shipments according to enterprise standards. The laboratory should internally associate personnel, equipment, and product information across the entire process, comprehensively applying quality data generated at various stages of production to upgrade business processes and truly facilitate the flow of quality data, thereby creating more value for the enterprise.
During the system requirements investigation phase, business personnel should clarify the requirements for the system interface. For relatively complex operational interfaces, it is advisable to use auxiliary software such as Axure for interface interaction design. A list of essential and non-essential functions for the LIMS system should be created, and system iterations should be completed progressively based on the IT department’s software development budget. Clear performance requirements for the system must be established to effectively handle the substantial data generated alongside products in production enterprises. If hardware configurations such as servers do not meet usage requirements, enterprise management efficiency will be severely impacted. Therefore, it is essential to clarify response times, throughput, processing times, and restrictions on primary and external storage. Additionally, requirements for system security, confidentiality, and reliability must be established, along with supporting software and data communication interfaces. Finally, reasonable feedback and responses must be implemented for system exceptions, such as illegal operations and array out-of-bounds errors. In the process of digital transformation, outdated management modes often “strangle information projects in their cradle” or “nurture them into giant babies.” Amid the trend of the IT industry penetrating manufacturing and assisting enterprises in digital transformation, enterprise managers should promptly introduce IT product development management models, document work at each node, conduct project reviews, and continuously improve the overall quality of the enterprise’s information technology team. Traditional enterprises often conduct IT projects in annual planning formats, which are severely mismatched with their cycles. Project managers can use Gantt charts or project review techniques to facilitate project implementation.
2.3 Technical Support and Requirements for Building the LIMS Platform
When designing the architecture of the laboratory system, it is essential to focus on the interface relationships between various modules and the information flow. First, clarify the system’s functional requirements for external time characteristics and communication specifications, and then develop corresponding modules within their respective platforms, which is an efficient system development approach. Repetitive business processes can reference the same SDK (Software Development Kit) and fully utilize programming software libraries, significantly enhancing system development efficiency. Considering the overall specifications of the LIMS system, which should be simple, reliable, and effective, the following requirements should be emphasized during the early stages of project construction:
2.3.1 Usability
The system should be operable through IE browsers, Google browsers, and an app, completing the required functional operations while exhibiting good operational speed and high data capacity. In a stable local area network environment, the system’s response time should meet requirements, and the average annual downtime of the entire system should be controlled to within 24 hours.
2.3.2 Reliability
A target database needs to be established to regularly back up laboratory data, preventing data loss due to employee errors. System permissions should be set according to the levels of business personnel to ensure data security and reliability, with complete emergency functions and recovery capabilities. Each business unit must check the correctness of user operation sequences and input data, prominently prompting error messages. The system should have an error handling mechanism, clearly indicating error messages and guiding users to address issues according to the system’s error handling manual. LIMS should back up system operation log files to track every user operation, allowing for recovery of the database’s previous state after a failure.
2.3.3 User-Friendliness
The system’s user interface should be friendly, with the homepage accommodating personalized settings. Functional modules should allow for customized operations based on user roles and tasks, ensuring ease of use.
2.3.4 Maintainability
The laboratory system should facilitate management and maintenance, allowing for easy entry of experimental data. Thus, the system must be modular, object-oriented, and extensible. It should provide management and maintenance for server systems, operating systems, application software, databases, and backup solutions.
2.3.5 Iterability
The system design should fully consider scalability, allowing for continuous upgrades based on the technical development and business needs of air conditioning enterprises. Due to the lack of top-level design at the initial stages, the gradual implementation of grassroots data collection has led to complex system construction, resulting in scattered and inconsistent data. This vast and intricate information has settled into “island” systems, reports, and databases across different business segments, making it challenging to achieve data sharing and application. To address this issue, one method involves implementing a top-down integration of business processes and system backends, creating a unified source of data and authorization. This approach requires a thorough review of existing systems, which is labor-intensive and difficult to implement. Another method is to introduce new technologies such as RPA (Robotic Process Automation). Currently, companies like Alibaba Cloud and Yisaiqi offer mature RPA solutions, and established software such as UiBot can automate cross-system data input and synchronization through pre-configured operational rules. This not only addresses the networking issues of legacy testing data but also facilitates the integration of data across various systems.
2.4 Data Middle Platform Centered on LIMS
Big data refers to datasets that are large, diverse, complex, rapidly growing, and cannot be processed using conventional methods. With the digital and intelligent development of quality inspection in manufacturing, the volume and variety of data are increasing rapidly. Internal systems across various business segments within enterprises upload vast amounts of experimental equipment status data and testing data. Additionally, there is a significant amount of experimental data, working condition data, production process data, personnel data, video monitoring data, etc. These data sources possess characteristics of heterogeneity, complexity, and multi-sourcing, thus falling under the category of big data. Therefore, the storage, layering, and empowerment of these data are challenging. As enterprises enter the era of “ubiquitous IoT,” various data have become one of the core values of enterprises, and for air conditioning laboratories, this data is particularly critical. In LIMS data processing, a layered approach is adopted: data collection layer, data application layer, and data empowerment layer. For data analysis and processing, products like FineBI from FanRuan can be utilized. When requirements change, a complete quality data warehouse has been established, allowing for direct switching of component styles and addition of data columns on data tables, meeting the usage requirements of personnel across various segments, with immediate effects on data presentation. The digital and intelligent construction of air conditioning energy efficiency laboratories under the background of enterprise digital transformation is a future development trend. The ultimate goal is to provide high-quality quality inspection services, enhance efficiency, upgrade testing methods, and improve product quality. This fundamentally involves re-engineering traditional inspection service processes, experimental equipment management, data collection methods, and production data monitoring using information and communication technology. Centered on air conditioning energy efficiency testing, this approach links frontend material inspection records with process inspection data, streamlining the timely handling of abnormal products and implementing intelligent quality warnings for production. Through an analysis of the current issues facing digital transformation in air conditioning enterprises and the formulation of countermeasures, this paper proposes an overall architecture for the LIMS platform in terms of system cognition, business data flow analysis, and technical requirements. By employing digital technologies, it aims to integrate and upgrade the business operations of air conditioning laboratories, enhancing the competitiveness of enterprises. Establishing a big data platform will facilitate the analysis, mining, and empowerment of air conditioning laboratory data, laying a solid foundation for the subsequent intelligent application of data in air conditioning laboratories.